Assessment of facial paralysis and gaze deviation

ABSTRACT

Images of an individual can be obtained and analyzed to determine an amount of facial paralysis of the individual. The images can also be analyzed to determine an amount of gaze deviation of the individual. The amount of facial paralysis of the individual and/or the amount of gaze deviation of the individual can be used to determine a probability that the individual experienced a biological condition.

PRIORITY CLAIM

This application claims priority to U.S. provisional patent applicationSer. No. 63/017,343, filed on Apr. 29, 2020, and entitled “AUTOMATICASSESSMENT OF FACIAL PARALYSIS AND GAZE DEVIATION”, which isincorporated by reference herein in its entirety

BACKGROUND

Various biological conditions can result in facial paralysis and/or gazedeviation. For example, cerebrovascular accidents, lesions of the brain,and nerve paralysis can result in gaze deviation. Additionally, Bell'spalsy, a brain tumor, tick bites, or stroke can cause facial paralysis.With respect to stroke, intracranial blood vessels supply nutrients andoxygen to the brain comprising the cerebrum, cerebellum and brainstem.Some arteries supply blood to anterior portions of the brain, whileother arteries supply blood to posterior portions of the brain.Disruption of the flow of blood to any part of the brain can haveserious effects. The flow of blood to the brain can be interrupted bythe narrowing and/or blockage of blood vessels supplying blood to thebrain. The disruption of the flow of blood to parts of the brain canimpair the function of the brain and result in numbness, weakness, orparalysis to parts of the body. Strokes can occur when blood supply to aportion of the brain is interrupted. Early detection and treatment of astroke can minimize the damage to the portion(s) of the brain whereblood supply was disrupted and minimize the aftereffects of the stroke.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some implementations areillustrated by way of example, and not limitation.

FIG. 1 is a diagrammatic representation of an architecture to determinemeasures of gaze deviation and facial paralysis based on imaging data,according to one or more example implementations.

FIG. 2 is a diagrammatic representation of an architecture to determinean amount of gaze deviation of an individual based on imaging data,according to one or more example implementations.

FIG. 3 is a diagrammatic representation of an architecture to determinean amount of facial paralysis of an individual based on imaging data,according to one or more example implementations.

FIG. 4 is a flowchart illustrating example operations of a process toanalyze an amount of gaze deviation and facial paralysis of anindividual to determine a probability of the individual experiencing abiological condition, according to one or more example implementations.

FIG. 5 includes a number of images that indicate individuals that arenot experiencing facial paralysis and individuals experiencing facialparalysis.

FIG. 6 includes images rendered using computed tomography data of a faceof an individual where the different images correspond to differentvantage points of the face of the individual.

FIG. 7 includes images indicating a location of an occluded blood vesselin an individual experiencing a stroke and the correlation between gazedeviation and the location of the occluded blood vessel.

FIG. 8 is a block diagram illustrating components of a machine, in theform of a computer system, that may read and execute instructions fromone or more machine-readable media to perform any one or moremethodologies described herein, in accordance with one or more exampleimplementations.

FIG. 9 is block diagram illustrating a representative softwarearchitecture that may be used in conjunction with one or more hardwarearchitectures described herein, in accordance with one or more exampleimplementations.

DETAILED DESCRIPTION

Various imaging techniques can be performed to detect the presence(currently or in the past) of a biological condition that results in atleast one of facial paralysis or stroke. In one or more examples, thebiological condition can include a neurological condition. In one ormore illustrative examples, the neurological condition can include astroke in the brain of an individual.

In one or more implementations, computed tomography (CT) imagingtechniques can be used to capture images of vessels that supply blood tothe brain of an individual. Additionally, magnetic resonance (MR)imaging techniques can be used to capture images of blood vessels of thebrain of an individual. Imaging techniques used to generate images ofvessels that supply blood to the brain of an individual can includeperfusion-based techniques and diffusion-based techniques. The images ofblood vessels of the brain of an individual generated by one or moreimaging techniques can be analyzed to determine regions of the brain ofthe individual in which an occluded vessel is present. Images of thebrain of an individual generated by one or more imaging techniques canalso be analyzed to identify regions of the brain of the individual thathave been damaged due to lack of blood supply due to a blood vesselocclusion.

In addition to imaging-based techniques, clinicians can provide adiagnosis of a stroke based on observations made during examinations ofthe individuals by the clinicians. In situations where an occluded bloodvessel causes a stroke, in addition to tissue damage that may occur dueto the lack of blood flow, the electrophysiology of an individual maynot function properly. In various examples, gaze deviation can be anindicator of stroke. Gaze deviation can be detected when an iris of atleast one eye of an individual is offset with respect to a referencelocation. The direction of the gaze deviation can indicate a location ofa stroke in the brain of the individual. For example, the gaze of anindividual can point in the same direction as the side of the brain inwhich an occluded vessel is located. To illustrate, gaze deviationtoward the right side (from the vantage point of facing the individual)can indicate an occluded vessel on the right side of the brain of theindividual and gaze deviation toward the left side (from the vantagepoint of facing the individual) can indicate an occluded vessel on theleft side of the brain of the individual.

Further, clinicians can observe facial paralysis in individualsexperiencing a stroke. In some situations, an occluded blood vessel inthe brain can cause paralysis of muscles in the face of an individualcausing the appearance of one or more regions of the face of theindividual to be altered. The changes in the facial appearance ofindividuals experiencing a stroke can result in facial droop. In variousexamples, muscles on the opposite side of the face can compensate forthe facial droop causing the appearance on the opposite side of the faceto also be altered. The side of the face in which the facial droop isoccurring can indicate that the location of the stroke caused by a largevessel occlusion is in the opposite hemisphere of the brain of theindividual. To illustrate, facial droop present on the right side of theface of an individual (from the vantage point of facing the individual)can indicate the presence of an occluded vessel in the left side of thebrain of the individual and facial droop present on the left side of theface of the individual (from the vantage point of facing the individual)can indicate the presence of an occluded vessel in the right side of thebrain of the individual.

Typically, images of a brain of an individual are analyzed by a trainedspecialist, such as a radiologist, to identify an occluded vessel and/orto determine damage to brain tissue. In some instances, radiologistsreceive medical records from clinicians that have examined theindividual, but these medical records often do not indicate physicalsymptoms of the individual. Additionally, radiologists typically areunable to physically examine the individuals whose images theradiologists are examining. The accuracy and expediency in which astroke is detected can impact the treatment administered to anindividual and, also, the prognosis of the recovery of the individual.Thus, tools, techniques, systems, and methods that can provideadditional information that can improve the accuracy of stroke detectioncan improve the treatment of the individuals and aid the recovery of theindividuals that have suffered or are suffering a stroke.

The techniques, systems, processes, and methods described herein aredirected to determining gaze deviation of individuals and facialparalysis of individuals based on imaging data. In various examples, theimaging data captured with respect to an individual can be analyzed withrespect to reference imaging data or training imaging data. A gazedeviation metric can be determined based on the imaging data thatindicates a measure of gaze deviation for the individual. Additionally,a facial paralysis metric can be determined based on the imaging datathat indicates a measure of facial paralysis of the individual. Invarious examples, the gaze deviation metric and/or the facial paralysismetric can be used to determine a probability of an individualexperiencing a stroke. The determination of metrics that indicatemeasures of gaze deviation and facial paralysis can help improve theaccuracy of an assessment performed by a radiologist with respect to thepresence or absence of an occluded vessel within a brain of anindividual. In one or more examples, the metrics indicating measures ofgaze deviation and facial paralysis can be used to augment imaginginformation that can indicate occlusions of blood vessels ofindividuals, such as images of blood vessels of the brains ofindividuals.

The imaging data used to determine the gaze deviation metrics and facialparalysis metrics can be captured by one or more imaging data sources.For example, the imaging data can be captured by one or more cameras ofa computing device, such as a mobile phone, smart phone, tabletcomputing device, wearable device, or a laptop computing device.Additionally, the imaging data can be captured by a CT imaging device oran MR imaging device. In scenarios where the imaging data is captured bya CT imaging device or an MR imaging device, the techniques, systems,processes, and methods described herein perform a 3D rendering thatcorresponds to the outer features of the head of the individual, such asfacial features of the individual. Thus, in contrast to the techniquesimplemented by existing systems that render the portions of the CT or MRimaging data that correspond to the inner portions of the brain of anindividual, such as the blood vessels and other tissue, the techniques,systems, methods, and processes described herein are directed torendering external features of the individual. In this way, aradiologist can have access to visual information with respect to anindividual, almost as if the patient is in front of them, that is notpresent in existing systems that simply provide internal images of thebrain of an individual. In various examples, coupling the externalimages of a face of an individual and metrics derived from the externalimages of the face of the individual with internal images of the brainof the individual can also increase the accuracy of diagnosis of theindividual with respect to the presence or absence of a stroke. Further,since the techniques, systems, methods, and processes described hereincan be implemented using a mobile computing device, an initialassessment with regard to facial paralysis and/or gaze deviation of apatient can be made by an emergency medical technician that is treatingthe patient. In this way, an initial assessment of the condition of thepatient can be made prior to the patient seeing a trained specialist,such as a neurologist or emergency room physician.

FIG. 1 is a diagrammatic representation of an architecture 100 todetermine measures of gaze deviation and facial paralysis based onimaging data, according to one or more example implementations. Thearchitecture 100 can include an image processing system 102. The imageprocessing system 102 can be implemented by one or more computingdevices 104. The one or more computing devices 104 can include one ormore server computing devices, one or more desktop computing devices,one or more laptop computing devices, one or more tablet computingdevices, one or more mobile computing devices, or combinations thereof.In certain implementations, at least a portion of the one or morecomputing devices 104 can be implemented in a distributed computingenvironment. For example, at least a portion of the one or morecomputing devices 104 can be implemented in a cloud computingarchitecture.

The image processing system 102 can obtain imaging data 106 that iscaptured with respect to an individual 108. The imaging data 106 caninclude one or more data files that include data related to imagescaptured by one or more imaging data sources. In one or more examples,the imaging data 106 can be formatted according to one or more imagingdata formats, such as a Digital Imaging and Communication in Medicine(DICOM) format. In one or more additional examples, the imaging data 106can be formatted according to at least one of a portable networkgraphics (PNG) format, a joint photographic experts group (JPEG) format,or a high efficient image format (HEIF). The imaging data 106 can berendered by the image processing system 102 to generate one or moreimages that can be displayed on a display device. In various examples,the imaging data 106 can include a series of images of the individual108 captured by one or more imaging data sources. In one or moreillustrative examples, the imaging data 106 can correspond to one ormore features of a head of the individual 108. In one or more scenarios,the imaging data 106 can be rendered to show external features of a headof the individual 108, such as at least a portion of a face of theindividual 108. In one or more further instances, the imaging data 106can be rendered to show internal features of the head of the individual108, such as blood vessels or brain tissue located within the head ofthe individual 108.

The imaging data 106 can be captured by an imaging apparatus 110. Theimaging device 106 can utilize one or more imaging technologies tocapture the images 104. In one or more examples, the imaging apparatus110 can implement computed tomography (CT) imaging techniques. In one ormore further examples, the imaging apparatus 110 can implement magneticresonance (MR) imaging techniques. MR imaging techniques implemented bythe imaging apparatus 110 can include perfusion-based MR imagingtechniques or diffusion-based MR imaging techniques. In situations wherethe imaging apparatus 110 implements CT-based imaging techniques orMR-based imaging techniques, the imaging data 106 can include thin-slicevolumetric data.

The one or more imaging data sources can also include one or morecomputing devices 112. The one or more computing devices 112 can includeat least one of a mobile computing device, a smart phone, a wearabledevice, a tablet computing device, or a laptop computing device. In oneor more examples, the one or more computing devices 112 can generate astructured light pattern on at least a portion of the face of theindividual 108 and capture one or more images of the individual 108 inrelation to the structured light pattern. In these instances, theimaging data 106 can include a point cloud. In one or more additionalexamples, the one or more computing devices 112 can include one or morelaser-based time-of-flight cameras. In these scenarios, the one or morecomputing devices 112 can include a light detecting and ranging (LiDAR)system. In one or more further examples, the one or more computingdevices 112 can include multiple cameras and the imaging data 106 cancorrespond to stereo vision images. In one or more examples, the imagingdata can also include random point clouds or sinusoidal patterns.

The image processing system 102 can include a gaze deviation analysissystem 114. The gaze deviation analysis system 114 can analyze theimaging data 106 to determine whether a gaze of the individual 108 isdifferent from the gaze of individuals that are not experiencing astroke. For example, the gaze deviation analysis system 114 candetermine a gaze deviation metric 116 for the individual 108. The gazedeviation metric 116 can indicate an offset of a gaze of at least oneeye of the individual 108. The offset of the gaze of an eye of theindividual 108 can be determined by analyzing a position of an iris ofthe eye of the individual 108 with respect to an expected position ofthe iris. The gaze deviation metric 116 can indicate an amount that theposition of the iris of the eye of the individual 108 is different fromthe expected position of the iris. In various examples, the gazedeviation analysis system 114 can determine an angular offset of an irisof the individual 108 in degrees, such as 0.5 degrees, 1 degree, 2degrees, 5 degrees, 10 degrees, 15 degrees, and so forth. The gazedeviation analysis system 114 can also determine a category indicatingan amount of gaze deviation for the individual 108. In one or moreillustrative examples, the gaze deviation metric 116 can indicatecategories, such as “No Deviation”, “Minor Deviation”, and “SignificantDeviation.” The gaze deviation metric 116 can also include adirectionality component, such as “Left” and “Right” indicating adirection that the iris of the individual 108 is pointing with respectto an expected direction. In these situations, the gaze deviation metric116 can indicate “Minor Deviation, Left”, “Minor Deviation, Right,” andthe like. In addition to the gaze deviation metric 116 being classifiedas categorical data, the gaze deviation metric can also be characterizedas continuous or ordinal data.

The image processing system 102 can also include a facial paralysisanalysis system 118. The facial paralysis analysis system 118 cananalyze the imaging data 106 to determine whether the individual 108 isexperiencing facial paralysis. In various examples, the facial paralysisanalysis system 118 can analyze an appearance of the face of theindividual 108 with respect to an expected appearance of faces ofindividuals that are not experiencing a stroke and an expectedappearance of faces of individuals that are experiencing a stroke. Invarious examples, the facial paralysis analysis system 118 can determinea measure of similarity between an appearance of the face of theindividual 108 with respect to the expected appearance of faces ofindividuals experiencing strokes and the expected appearance of faces ofindividuals that are not experiencing a stroke. In one or moreillustrative examples, the facial paralysis analysis system 118 cananalyze target regions of the face of the individual 108 to generate afacial paralysis metric 120. The facial paralysis metric 120 canindicate whether the characteristics of target regions of the face ofthe individual 108 are more like the characteristics of target regionsof faces of individuals that are not experiencing a stroke or more likethe characteristics of target regions of faces of individuals that areexperiencing a stroke. The facial paralysis metric 120 can include anumerical metric indicating an amount of difference betweencharacteristics of target regions of the face of the individual 108 andcharacteristics of target regions of faces of individuals experiencing astroke. In various examples, the facial paralysis metric 120 can includecategorical values indicating an amount of difference betweencharacteristics of target regions of the face of the individual 108 andcharacteristics of target regions of faces of individuals experiencing astroke. To illustrate, the facial paralysis metric 120 can include “NoParalysis”, “Minor Paralysis”, or “Significant Paralysis.” The facialparalysis metric 120 can also include a directionality component, suchas “Left” and “Right” indicating a side of the face of the individual108 in which facial paralysis may be present. In these situations, thefacial paralysis metric can indicate “Minor Paralysis, Left”, “MinorParalysis, Right,” and the like.

The gaze deviation metric 116 and the facial paralysis metric 120 can beprovided to an assessment system 122 of the image processing system 102.The assessment system 122 can analyze the gaze deviation metric 116 andthe facial analysis metric 120 to determine a probability of theindividual 108 experiencing a biological condition, such as a stroke orBell's palsy. A probability of the individual 108 experiencing abiological condition can have a higher value based on a relativelyhigher value for at least one of the gaze deviation metric 116 or thefacial paralysis metric 120. Additionally, a probability of theindividual 108 experiencing a biological condition can have a lowervalue based on a relatively lower value for at least one of the gazedeviation metric 116 or the facial paralysis metric 120.

In one or more examples, the image processing system 102 can generatesystem output 124. The system output 124 can include an assessmentmetric 126 that corresponds to a probability that the individual 108 isexperiencing a biological condition. As used herein, a biologicalcondition can refer to an abnormality of function and/or structure in anindividual to such a degree as to produce or threaten to produce adetectable feature of the abnormality. A biological condition can becharacterized by external and/or internal characteristics, signs, and/orsymptoms that indicate a deviation from a biological norm in one or morepopulations. A biological condition can include at least one of one ormore diseases, one or more disorders, one or more injuries, one or moresyndromes, one or more disabilities, one or more infections, one or moreisolated symptoms, or other atypical variations of biological structureand/or function of individuals. In one or more illustrative examples,the one or more biological conditions can include one or moreneurological conditions. In various examples, the system output 124generated by the image processing system 102 can include user interfacedata that corresponds to a user interface that includes the assessmentmetric 126. The assessment metric 126 can indicate a numerical value ofthe probability of a biological condition being present in theindividual 108. In one or more additional examples, the assessmentmetric 126 can indicate a categorical value of the probability of abiological condition being present in the individual 108. For example,the assessment metric 126 can include “No Biological Condition”, “MinorBiological Condition Probability”, “Significant Biological ConditionProbability.” In one or more illustrative examples, the assessmentmetric 126 can include “No Stroke”, “Minor Stroke Probability”,“Significant Stroke Probability.”

The system output 124 can also include one or more rendered images 128.The one or more rendered images 128 can include one or more images ofexternal features of the individual 108. For example, the one or morerendered images 128 can include a first image 130 of a face of theindividual 108. Additionally, the one or more rendered images 128 caninclude one or more images of internal features of the individual 108.To illustrate, the one or more rendered images 128 can include a secondimage 132 of blood vessels of the brain of the individual 108. In one ormore illustrative examples, the system output 124 can include a userinterface that includes the first image 130 or the second image 132. Inone or more additional examples, the system output 124 can include auser interface that includes both the first image 130 and the secondimage 132.

Additionally, in one or more further scenarios, the image processingsystem 102 can determine the assessment metric 126 based on an analysisof external features of the head of the individual 108 and internalfeatures of the head of the individual 108. For example, the imageprocessing system 102 can analyze characteristics of blood vessels ofthe brain of the individual 108 to determine a probability of theindividual 108 experiencing a stroke. The image processing system 102can combine the probability of a stroke being present in the individual108 based on an analysis of blood vessels of the brain of the individualwith a probability of a stroke being present in the individual 108determined by the assessment system 122. In these instances, the outputgenerated by the assessment system 122 can be complementary to ananalysis by the image processing system of blood vessels of the brain ofthe individual 108. In various examples, combining the probability of astroke being present in the individual 108 determined by the assessmentsystem 122 with a probability of a stroke being present in theindividual 108 determined based on an analysis of blood vessels of thebrain of the individual 108 can increase an accuracy of the imageprocessing system 102 in determining a probability of a stroke beingpresent in the individual 108.

Although the illustrative example of FIG. 1 shows that the imageprocessing system 102 includes both the gaze deviation analysis system114 and the facial paralysis analysis system 118, in one or moreadditional implementations, the image processing system 102 can includethe gaze deviation analysis system 114 or the facial paralysis system118. In scenarios where the image processing system 102 includes thegaze deviation analysis system 114 and not the facial paralysis analysissystem 118, the assessment system 122 can determine the assessmentmetric 126 using the gaze deviation metric 116 and not the facialparalysis metric 120. In additional instances where the image processingsystem 102 includes the facial paralysis analysis system 118 and not thegaze deviation analysis system 114, the assessment system 122 candetermine the assessment metric 126 using the facial paralysis metric120 and not the gaze deviation metric 116.

FIG. 2 is a diagrammatic representation of an architecture 200 todetermine an amount of gaze deviation of an individual based on imagingdata, according to one or more example implementations. The architecture200 can include the gaze deviation analysis system 114. The gazedeviation analysis system 114 can include one or more gaze deviationmodels 202. The one or more gaze deviation models 202 can be implementedto generate the gaze deviation metric 116. The one or more gazedeviation models 202 can be generated using one or more machine learningtechniques. The one or more machine-learning techniques can include oneor more classification-based machine learning techniques. In variousexamples, the one or more machine learning techniques can includesupervised classification-based machine learning techniques. The one ormore supervised classification-based machine learning techniques caninclude at least one of one or more logistic regression algorithms, oneor more Naïve Bayes algorithms, one or more K-nearest neighboralgorithms, or one or more support vector machine algorithms. In one ormore additional examples, the one or more machine learning techniquesused to generate the one or more gaze deviation models 202 can includeone or more residual neural networks. In one or more further examples,the one or more machine learning techniques used to generate the one ormore gaze deviation models 202 can include at least one of one or moreu-net convolutional networks, one or more v-net convolutional neuralnetworks, or one or more residual neural networks (ResNet). In one ormore illustrative example, a combination of machine learning techniquescan be used to generate the one or more gaze deviation models 202. Toillustrate, one or more residual neural networks and one or more u-netconvolutional networks can be used to generate the one or more gazedeviation models 202.

In one or more implementations, the one or more gaze detection models202 can be training using training data 204. The training data 204 caninclude image training data 206. In various examples, the image trainingdata 206 can include images of faces of a number of differentindividuals. At least a portion of the image training data 206 caninclude images captured of individuals that experienced a biologicalcondition. Additionally, at least a portion of the image training data206 can include images captured of individuals that did not experience abiological condition. In one or more illustrative examples, the trainingdate 206 can include a series of images of a number of individuals thatmove their eyes is various directions and to varying degrees. Thetraining data 204 can also include classification data 208 indicatingrespective images included in the image training data 206 thatcorrespond to individuals that experienced a biological condition andadditional images included in the image training data 206 thatcorrespond to individuals that did not experience a biologicalcondition. Further, the training data 204 can include a number of imagesthat can be used to validate the one or more gaze deviation models 202.In one or more illustrative examples, a convolutional neural networkhaving an 80/20 split using 5-fold validation can be trained using thetraining data 206 to generate the one or more gaze deviation models 202.

In various examples, the one or more gaze deviation models 202 can betrained to identify images in which a gaze of an individual is offsetwith respect to an expected gaze. In one or more examples, the expectedgaze can correspond to the gaze of individuals in which a stroke is notpresent when the individuals are looking straight forward at an object.The one or more gaze deviation models 202 can determine an expected gazeof an individual by analyzing images included in the image training data206 of individuals in which a biological condition is not present whoare looking straight forward at an object to determine features of theeyes of the individuals. In one or more illustrative examples, the oneor more gaze deviation models 202 can determine that an expected gaze ofan individual in which a biological is not present is a gaze in whichthe irises of the individual are aligned with respective vertical axes.FIG. 2 includes an example expected gaze 210 with a first iris 212 and asecond iris 214 being aligned along a first vertical axis 216 and asecond vertical axis 218, respectively.

The one or more gaze deviation models 202 can also be trained todetermine features of eyes of individuals in which gaze deviation ispresent. Gaze deviation can be determined as a gaze of an individualthat has at least a threshold amount of difference from the expectedgaze. The threshold amount of difference can include at least a minimumangular offset in relation to a respective vertical axis. The minimumangular offset can be at least about 0.5 degrees, at least about 1degree, at least about 2 degrees, at least about 3 degrees, at leastabout 4 degrees, or at least about 5 degrees. In one or moreillustrative examples, the minimum angular offset can be from about 0.5degrees to about 5 degrees, from about 1 degree to about 3 degrees, orfrom about 0.5 degrees to about 2 degrees. After training and validationof the one or more gaze deviation models 202, the one or more gazedeviation models 202 can be implemented to classify the imaging data 106of the individual 108.

The one or more gaze deviation models 202 can generate the gazedeviation metric 116 based on the imaging data 106. The illustrativeexample of FIG. 2 includes an example gaze 220 derived from the imagingdata 106. The example gaze 220 includes a third iris 220 and a fourthiris 222. The example gaze 220 indicates that the third iris 220 isoffset with respect to the first vertical axis 216 by an angular offset224. The example gaze 220 also indicates that the fourth iris 222 isaligned with the second vertical axis 222 with no angular offset. Theone or more gaze deviation models 202 can determine whether the angularoffset 224 is greater than a minimum angular offset. In situations wherethe angular offset 224 is greater than the minimum angular offset, theone or more gaze deviation models 202 can classify the individual 108 ashaving a gaze deviation that corresponds to at least a minimumprobability of the individual 108 experiencing a stroke. In variousexamples, the extent of the angular offset 224 beyond the minimumangular offset can indicate the magnitude of the probability of a strokebeing present in the individual 108. For example, as the angular offset224 increases, a probability of the individual 108 experiencing a strokecan also increase. Additionally, as the angular offset 224 increases, aseverity of a stroke can also increase. Although the illustrativeexample of FIG. 2 indicates the angular offset 224 with respect to thethird iris 220, in other examples, an additional angular offset can bepresent with respect to the fourth iris 222. Additionally, both thethird iris 220 and the fourth iris 222 can be offset. In one or moreexamples, the additional angular offset of the fourth iris 222 can bethe same as the angular offset 224. In one or more further examples, theangular offset of the fourth iris 222 can be different from the angularoffset 224.

In one or more additional examples, the one or more gaze deviationmodels 202 can determine an amount of gaze deviation of the individual108 based on reference image data 210. In one or more examples, thereference image data 210 can include one or more images of theindividual 108 captured during a period of time in which the individual108 was not experiencing a biological condition. In these scenarios, theone or more gaze deviation models 202 can analyze the imaging data 106with respect to the reference image data 210 to determine an amount ofdifference between the gaze of the individual 108 included in thereference image data 210 and the gaze of the individual 108 included inthe imaging data 106. In various examples, the expected gaze 210 cancorrespond to the gaze of the individual 108 determined based on thereference image data 210.

In one or more examples, before being processed by the one or more gazedeviation models 202, the training data 204 and the imaging data 106 canbe preprocessed by the gaze deviation analysis system 114 or the imageprocessing system 102 of FIG. 1. For example, one or more computationaltechniques can be applied to the training data 204 and the imaging data106 to generate a modified dataset that is processed according to theone or more machine learning techniques implemented by the one or moregaze deviation models 202. In scenarios where at least one of thetraining data 204 or the imaging data 106 includes three-dimensionalvolumetric data from CT-based imaging devices or MR-based imagingdevices, a 3-dimensional (3D) rendering can be performed. A 3D renderingcan also be performed with in respect to data captured via a LiDARsystem, a time of flight system, and/or a point cloud/structured lightsystem. The 3D rendering can include a volume rendering or a surfacerendering. In one or more illustrative examples, a binary mask can begenerated from images included in at least one of the training data 204or the imaging data 106. The binary mask can indicate voxels of theimages that are foreground voxels and background voxels. Additionally, amesh of polygons can be generated using the binary mask that correspondsto a surface of an object included in the images, such as the face ofindividuals that correspond to the images. The mesh of polygons can berendered to produce images of the faces of individuals that can beanalyzed by the one or more gaze deviation models 202.

Images derived from the training data 204 and the imaging data 106 canalso be processed to identify the eyes of individuals. In one or moreexamples, the classification data 208 can identify the eyes ofindividuals included in images of the image training data 206. In thisway, the one or more gaze deviation models 202 can be trained toidentify the eyes of individuals. In one or more additional examples, afilter can be generated that can be used to identify the eyes ofindividuals. The filter to identify the eyes of individuals can begenerated based on the training data 204. In various examples, the gazedeviation analysis system 114 can determine that at least one eye of theindividual 108 is not able to be identified. In these situations, thegaze deviation analysis system 114 can provide an indication that thegaze deviation analysis system 112 is unable to analyze the imaging data106. In one or more examples, at least one eye of the individual 108 canbe blocked by an object, such as sunglasses, hair, and the like.

Additionally, at least one of the training data 204 or the imaging data106 can be augmented before being processed by the one or more gazedeviation models 202. In one or more examples, volumetric data obtainedfrom MR-based imaging devices, CT-based imaging devices, LiDAR systems,time of flight systems, structured light systems, or images captured bya mobile device camera from a number of different vantage points can beused to generate a number of images that correspond to multiple vantagepoints. In this way, the gaze of individuals and facial paralysis can beanalyzed from a number of vantage points. In one or more additionalexamples, one or more vantage points can be identified that provide animage of the gaze of the individuals that can be processed by the one ormore gaze deviation models 202 to increase the accuracy of the gazedeviation metric 116. For example, one or more images related to avantage point showing the individual facing forward and looking forwardcan be selected for processing by the one or more gaze deviation modelsrather than images with vantage points where individuals are looking toone side or are not facing forward. In one or more further examples, atleast one of the training data 204 or the imaging data 106 can includemultiple images of individuals corresponding to different vantagepoints. To illustrate, one or more cameras of a computing device can beused to obtain images of individuals from different vantage points (inrelation to the orientation of the computing device), such as one ormore right side vantage points, one or more left side vantage points, ora front facing vantage point. In these instances, images correspondingto forward facing vantage points can be selected for analysis by the oneor more gaze deviation models 202.

Although the gaze deviation of an individual is discussed with respectto FIG. 2 in relation to an iris of an individual, gaze deviation canalso be determined based on a location of a pupil of an individual.

FIG. 3 is a diagrammatic representation of an architecture 300 todetermine an amount of facial paralysis of an individual, according toone or more example implementations. The architecture 300 can includethe facial paralysis analysis system 118. The facial paralysis analysissystem 118 can include one or more facial paralysis models 302. The oneor more facial paralysis models 302 can be implemented to generate thefacial paralysis metric 120. The one or more facial paralysis models 302can be generated using one or more machine learning techniques. The oneor more machine-learning techniques can include one or moreclassification-based machine learning techniques. In various examples,the one or more machine learning techniques can include supervisedclassification-based machine learning techniques. The one or moresupervised classification-based machine learning techniques can includeat least one of one or more logistic regression algorithms, one or moreNaïve Bayes algorithms, one or more K-nearest neighbor algorithms, orone or more support vector machine algorithms. In one or more additionalexamples, the one or more machine learning techniques used to generatethe one or more facial paralysis models 302 can include one or moreresidual neural networks. In one or more further examples, the one ormore machine learning techniques used to generate the one or more facialparalysis models 302 can include one or more u-net convolutionalnetworks, one or more v-net convolutional neural networks, or one ormore residual neural networks (ResNet). In one or more illustrativeexample, a combination of machine learning techniques can be used togenerate the one or more facial paralysis models 302. To illustrate, oneor more residual neural networks and one or more u-net convolutionalnetworks can be used to generate the one or more facial paralysis models302.

In one or more implementations, the one or more facial paralysis models302 can be trained using training data 304. The training data 304 caninclude image training data 306. In various examples, the image trainingdata 306 can include images of faces of a number of differentindividuals. At least a portion of the image training data 306 caninclude images captured of individuals experiencing a biologicalcondition. Additionally, at least a portion of the image training data306 can include images captured of individuals that are not experiencinga biological condition. The training data 304 can also includeclassification data 308 indicating respective images included in theimage training data 306 that correspond to individuals experiencing abiological condition and additional images included in the imagetraining data 306 that correspond to individual that are notexperiencing a biological condition. Further, the training data 304 caninclude a number of images that can be used to validate the one or morefacial paralysis models 302. In one or more illustrative examples, aconvolutional neural network having an 80/20 split using 5-foldvalidation can be trained using the training data 306 to generate theone or more facial paralysis models 302.

In various examples, the one or more facial paralysis models 302 can betrained to identify target regions of faces of individuals. The targetregions can be regions of faces of individuals that can indicate facialparalysis. The one or more facial paralysis models 302 can be trained todetermine characteristics of the target regions that indicate facialparalysis. In one or more examples, the one or more facial paralysismodels can be trained to determine an amount of facial droop on one sideof the faces of individuals. The one or more facial paralysis models 302can analyze images of individuals that are not experiencing a stroke andimages of individuals that are experiencing a stroke to determinedifferences in characteristics of the target regions in individualsexperiencing a stroke and those that are not experiencing a stroke. Inone or more scenarios, the characteristics of target regions ofindividuals that are experiencing a stroke can be shifted with respectto characteristics of target regions of individuals that are notexperiencing a stroke. The facial paralysis metric 120 can indicate anamount of similarity between characteristics of target regions ofindividuals included in images being analyzed by the facial paralysisanalysis system 118 with respect to characteristics of target regions ofindividuals that are experiencing a stroke. In one or more illustrativeexamples, the facial paralysis metric 120 can indicate a measure offacial paralysis of individuals, such as at least one of a numericalvalue or a categorical value.

In this way, the one or more facial paralysis models 302 can analyze theimage data 106 to determine whether the characteristics of the targetregions of the face of the individual 108 correspond to characteristicsof target regions of individuals experiencing a biological condition orcorrespond to characteristics of target regions of individuals that arenot experiencing a biological condition. In the illustrative example ofFIG. 3, an image 312 can be rendered based on the image data 106. Theone or more facial paralysis models 302 can analyze characteristics of anumber of target regions of the face of the individual 108 including afirst target region 314, a second target region 316, a third targetregion 318, a fourth target region 320, and a fifth target region 322.The one or more facial paralysis models 302 can analyze the targetregions 314, 316, 318, 320, 322 to determine one or more characteristicsof the target regions 314, 316, 318, 320, 322 of the individual 108. Inaddition, the one or more facial paralysis models 302 can determine thefacial paralysis metric 120 for the individual 108 based on thecharacteristics of the target regions 314, 316, 318, 320, 322 for theindividual 108.

In one or more additional examples, the one or more facial paralysismodels 302 can determine an amount of facial paralysis of the individual108 based on reference image data 310. In one or more examples, thereference image data 310 can include one or more images of theindividual 108 captured during a period of time in which the individual108 was not experiencing a stroke. In these scenarios, the one or morefacial paralysis models 302 can analyze the imaging data 106 withrespect to the reference image data 310 to determine an amount ofdifference between the characteristics of the target regions 314, 316,318, 320, 322 of the individual 108 included in the reference image data310 and the characteristics of the target regions 314, 316, 318, 320,322 of the individual 108 that are determined based on the imaging data106.

Further, the one or more facial paralysis models 302 can determine anamount of facial paralysis of the individual 108 by analyzingcharacteristics of at least a portion of the target regions 314, 316,318, 320, 322 on opposing sides of the face of the individual 108. Forexample, the one or more facial paralysis models 302 can analyzecharacteristics of the first target region 314 with respect to thecharacteristics of the fifth target region 322. Additionally, the one ormore facial paralysis models 302 can analyze characteristics of thesecond target region 316 with respect to the characteristics of thefourth target region 320, the one or more facial paralysis models 302can determine an amount of facial paralysis of the individual 308 basedon an amount of differences between the characteristics of the firsttarget region 314 and the fifth target region 322 and/or an amount ofdifferences between the characteristics of the second target region 316and the fourth target region 320.

In one or more examples, before being processed by the one or morefacial paralysis models 302, the training data 304 and the imaging data106 can be preprocessed by the facial paralysis analysis system 118 orthe image processing system 102 of FIG. 1. For example, one or morecomputational techniques can be applied to the training data 304 and theimaging data 106 to generate a modified dataset that is processedaccording to the one or more machine learning techniques implemented bythe one or more facial paralysis models 302. In scenarios where at leastone of the training data 304 or the imaging data 106 includesthree-dimensional volumetric data from CT-based imaging devices orMR-based imaging devices, a 3-dimensional rendering can be performed. Inone or more illustrative examples, a binary mask can be generated fromimages included in at least one of the training data 304 or the imagingdata 106. The binary mask can indicate voxels of the images that areforeground voxels and background voxels. Additionally, a mesh can begenerated using the binary mask that corresponds to a surface of anobject included in the images, such as the face of individuals thatcorrespond to the images. The mesh can be rendered to produce images ofthe faces of individuals that can be analyzed by the one or more facialparalysis models 302. In various examples, the mesh can include a meshof polygons. Additionally, the 3D rendering can include volume renderingtechniques or surface rendering techniques. In one or more additionalexamples, the preprocessing can be performed without generating thebinary mask.

Images derived from the training data 304 and the imaging data 106 canalso be processed to identify the target regions, such as target regions314, 316, 318, 320, 322, of individuals. In one or more examples, theclassification data 308 can identify the target regions. In this way,the one or more facial paralysis models 302 can be trained to identifythe target regions of individuals. In one or more additional examples, afilter can be generated that can be used to identify the target regionsof individuals. The filter to identify the target regions of individualscan be generated based on the training data 304. In various examples,the facial paralysis analysis system 118 can determine that at least oneof the target regions 314, 316, 318, 320, 322 of the individual 108 isnot able to be identified or is distorted. In these situations, thefacial paralysis analysis system 118 can provide an indication that thefacial paralysis analysis system 118 is unable to analyze the imagingdata 106. In one or more examples, at least one eye of the individual108 can be blocked by an object, such as an oxygen mask, glasses, andthe like.

Additionally, at least one of the training data 304 or the imaging data106 can be augmented before being processed by the one or more facialparalysis models 302. In one or more examples, volumetric data obtainedfrom MR-based imaging devices or CT-based imaging devices can be used togenerate a number of images that correspond to multiple vantage points.In this way, the characteristics of target regions of individuals can beanalyzed from a number of vantage points. In one or more additionalexamples, one or more vantage points can be identified that provide animage of at least a portion of the target regions of the individualsthat can be processed by the one or more facial paralysis models 302 toincrease the accuracy of the facial paralysis metric 120. For example,one or more images related to a vantage point showing the individualfacing forward and looking forward can be selected for processing by theone or more facial paralysis models 302 rather than images with vantagepoints where individuals are looking to one side or are not facingforward. In one or more further examples, at least one of the trainingdata 304 or the imaging data 106 can include multiple images ofindividuals corresponding to different vantage points. To illustrate,one or more cameras of a computing device can be used to obtain imagesof individuals from different vantage points (in relation to theorientation of the computing device), such as one or more right sidevantage points, one or more left side vantage points, or a front facingvantage point. In these instances, images corresponding to forwardfacing vantage points can be selected for analysis by the one or morefacial paralysis models 302. In various examples, a computing devicebeing used to capture the image data 106 for the individual 108 and/orto capture the image training data 306 can include a user interfaceguide, such as an outline, that enables a user of the computing deviceto align the faces of individuals with the user interface guide. In thisway, the image training data 306 and the image data 106 can bestandardized resulting in the image data 106 being processed moreaccurately and efficiently by the facial paralysis analysis system 118.

FIG. 4 illustrate flowcharts of processes to analyze images of faces ofindividuals to generate an assessment of whether the individuals areexperiencing a stroke. The processes may be embodied incomputer-readable instructions for execution by one or more processorssuch that the operations of the processes may be performed in part or inwhole by the functional components of the image processing system 102.Accordingly, the processes described below are by way of example withreference thereto, in some situations. However, in otherimplementations, at least some of the operations of the processesdescribed with respect to FIG. 4 may be deployed on various otherhardware configurations. The processes described with respect to FIG. 4are therefore not intended to be limited to the image processing system102 and can be implemented in whole, or in part, by one or moreadditional components. Although the described flowcharts can showoperations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations may be re-arranged. A process is terminated when itsoperations are completed. A process may correspond to a method, aprocedure, an algorithm, etc. The operations of methods may be performedin whole or in part, may be performed in conjunction with some or all ofthe operations in other methods, and may be performed by any number ofdifferent systems, such as the systems described herein, or any portionthereof, such as a processor included in any of the systems.

FIG. 4 is a flowchart illustrating example operations of a process 400to analyze an amount of gaze deviation and facial paralysis of anindividual to determine a probability of the individual experiencing astroke, according to one or more example implementations. At operation402, the process 400 can include obtaining imaging data from one or moreimaging sources. The imaging data can correspond to at least a portionof a face of an individual. The one or more imaging sources can includea MR-based imaging apparatus or a CT-based imaging apparatus. TheCT-based imaging apparatus can include, in one or more examples, aCT-angiogram imaging apparatus or a CT-perfusion imaging apparatus.Additionally, the one or more imaging sources can include one or morecomputing devices, such as a smart phone, a wearable device, a mobilecomputing device, a laptop computing device, or a tablet computingdevice. In these scenarios, the imaging data can be captured by one ormore cameras of the one or more computing devices. In various examples,a stereoscopic camera system of the one or more computing devices can beused to capture the imaging data. In one or more additional examples,the one or more computing devices can generate a structured lightpattern and capture one of more images of the individual with respect tothe structured light pattern. The one or more imaging sources can alsoinclude time of flight cameras, LiDAR systems, and/or stereovisioncameras.

At operation 404, the process 400 can include determining, based on theimage data, a gaze deviation metric for the individual. The gazedeviation metric can indicate an offset of a location of at least oneiris of the individual with respect to an expected iris location. Inaddition, the process 400 can include, at operation 406, determining,based on the imaging data, a facial paralysis metric of the individual.The facial paralysis metric can indicate differences between anappearance of one or more target regions of the face of the individualwith respect to an expected appearance of the one or more targetregions.

In various examples, one or more machine learning techniques can beimplemented to determine the gaze deviation metric and the facialparalysis metric. In one or more examples, one or more convolutionalneural networks can be trained to determine the gaze deviation metricand the facial paralysis metric. The training data for the one or moreconvolutional neural networks can include a first set of images of firstindividuals that experienced a biological condition that resulted in anamount of gaze deviation. The training data for the one or moreconvolutional neural networks can also include a second set of images ofsecond individuals that experienced biological condition that resultedin an amount of facial paralysis. Further, the training data for the oneor more convolutional neural networks can include a third set of imagesof third individuals that did not experience a biological condition,that did not have an amount of gaze deviation, and that did not have anamount of facial paralysis. The training data can also include anadditional set of images of an additional group of individuals thatexperienced both gaze deviation and facial paralysis. The training datacan also include characteristics of the individuals, such as ethnicbackground, age, gender, presence of glasses, presence of facial hair,and the like. In one or more further examples, the training data can beobtained from a same individual captured at a previous point in timebefore the individual was experiencing the biological condition. In oneor more examples, the training data can include classification dataindicating the images where gaze deviation is present, the images wherefacial paralysis is present, and the images where neither gaze deviationnor facial paralysis are present. In one or more illustrative examples,the output of the one or more convolutional networks can include aplurality of categories with respect to gaze deviation and a pluralityof categories with respect to facial paralysis. In one or more furtherexamples, one or more convolutional neural networks can be trained todetermine a feature set that is indicative of gaze deviation and afeature set that is indicative of facial paralysis. The feature set maynot include one or more target regions. In these scenarios, the one ormore convolutional neural networks can analyze new images of patientswith respect to the respective feature sets to determine amounts ofsimilarity between the feature sets and the new images to determine ameasure of gaze deviation and/or a measure of facial paralysis.

In one or more examples, training data can be determined from a numberof vantage points. The vantage points can include images taken from anumber of different positions with respect to an individual. The numberof vantage points can also be determined in silico, such as extracted MRor CT data related to the different vantage points.

The process 400 can also include, at operation 408, analyzing at leastone of the gaze deviation metric or the facial paralysis metric todetermine an assessment metric indicating a probability of a presence ofa biological condition with respect to the individual. In variousexamples, as the gaze deviation metric increases, the assessment metriccan also increase because the more pronounced the gaze deviation for theindividual, the higher the probability that the individual isexperiencing the biological condition. Additionally, as the facialparalysis metric increases, the assessment metric can also increasebecause as the amount of facial paralysis increases, the probabilitythat the individual is experiencing a biological condition alsoincreases. The amount of gaze deviation and/or the amount of facialparalysis can also be indicators of severity of a biological conditionexperienced by the individual. In one or more examples, the biologicalcondition can include a neurological condition, such as Bell's palsy orstroke.

Further, at operation 410, the process 400 can include generating userinterface data corresponding to one or more user interfaces that includea user interface element indicating the probability of the presence ofthe biological condition with respect to the individual. In one or moreexamples, the assessment metric can be displayed in the user interfaceas a numerical probability. In one or more additional examples, theassessment metric can be displayed in the user interface as acategorical indicator. The one or more user interfaces can also includean image of the face of the individual that is rendered based on theimaging data and on which the gaze deviation metric and the facialparalysis metric are determined. In addition to the facial image of theindividual an image of the brain of the individual can also bedisplayed. For example, an image showing blood vessels of the brain ofthe individual can also be displayed in the one or more user interfaces.In this way, images of external portions of individuals and images ofinternal portions of individuals can be rendered from the same imagingdata and used to determine a probability of an individual experiencing abiological condition.

In one or more illustrative examples, the images can be captured of anindividual using a mobile computing device, such an EMT or other medicalpersonnel. The screen of the mobile computing device can include anoutline and the image of the individual shown in the user interface canbe aligned with the outline. In one or more examples, a number of imagescan be captured from different vantage points. In these scenarios, theimage data captured by the mobile computing device can be analyzed todetermine an amount of facial paralysis of the individual and/or anamount of gaze deviation of the individual. Additionally, an initialassessment of the individual can be determined with respect to abiological condition can be determined based on the amount of gazedeviation and/or the amount of facial paralysis.

FIG. 5 includes a number of images that indicate individuals that arenot experiencing facial paralysis and individuals experiencing facialparalysis. Image A includes an image of an individual prior toexperiencing a stroke and Image B includes an image of the sameindividual after experiencing a stroke with left side facial paralysisand gaze deviation in the left eye that is to the right. Image Cincludes a volume rendering of CT volume data of an 88-year oldindividual experiencing a right side proximal intracranial artery (ICA)occlusion with facial paralysis on the left side. Image D includes avolume rendering of CT volume data of an 82-year old patient withocclusion of the right superior M2 region of the middle cerebral artery(MCA) with left facial paralysis and mild conjugate gaze deviation tothe right side. Image E includes a photographic overlay of the face ofan individual on a surface rendering obtained with a three-dimensionalstructured light camera.

FIG. 6 includes images rendered using computer tomography data of a faceof an individual where the different images correspond to differentvantage points of the face of the individual. The images included inFIG. 6 are from 9 different vantage points rendered from CT volume dataof an 80-year old patient with an occlusion in the right distal portionof the ICA.

FIG. 7 includes images indicating a location of an occluded blood vesselin an individual experiencing a stroke and the correlation between gazedeviation and the location of the occluded blood vessel. Image A is adiagram showing the location of an example patient with a stroke locatedin the right hemisphere of the brain and having corresponding conjugategaze deviation to the right. Image B includes a non-contrast CT image ofan 80-year old patient with a distal ICA occlusion causing a largecerebral blood flow deficit in the right middle cerebral arteryterritory resulting in conjugate gaze deviation to the right. Gazedeviation can be determined based on the non-contrast CT data. Image Cincludes an example eyeball extraction by segmentation from the data ofthe non-contrast CT image and the corresponding location of the lenses.

FIG. 8 is a block diagram illustrating components of a machine 800,according to some example implementations, able to read instructionsfrom a machine-readable medium (e.g., a machine-readable storage medium)and perform any one or more of the methodologies discussed herein.Specifically, FIG. 8 shows a diagrammatic representation of the machine800 in the example form of a computer system, within which instructions802 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 800 to perform any one ormore of the methodologies discussed herein may be executed. As such, theinstructions 802 may be used to implement modules or componentsdescribed herein. The instructions 802 transform the general,non-programmed machine 800 into a particular machine 800 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative implementations, the machine 800 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 800 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 800 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, at networkswitch, a network bridge, or any machine capable of executing theinstructions 802, sequentially or otherwise, that specify actions to betaken by machine 800. Further, while only a single machine 800 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 802 to perform any one or more of the methodologiesdiscussed herein.

The machine 800 may include processors 804, memory/storage 806, and I/Ocomponents 808, which may be configured to communicate with each othersuch as via a bus 810. “Processor” in this context, refers to anycircuit or virtual circuit (a physical circuit emulated by logicexecuting on an actual processor 804) that manipulates data valuesaccording to control signals (e.g., “commands,” “op codes,” “machinecode,” etc.) and which produces corresponding output signals that areapplied to operate a machine 800. In an example implementation, theprocessors 804 (e.g., a central processing unit (CPU), a reducedinstruction set computing (RISC) processor, a complex instruction setcomputing (CISC) processor, a graphics processing unit (GPU), a digitalsignal processor (DSP), an application-specific integrated circuit(ASIC), a radio-frequency integrated circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 812 and a processor 814 that may execute the instructions 802.The term “processor” is intended to include multi-core processors 804that may comprise two or more independent processors (sometimes referredto as “cores”) that may execute instructions 802 contemporaneously.Although FIG. 8 shows multiple processors 804, the machine 800 mayinclude a single processor 812 with a single core, a single processor812 with multiple cores (e.g., a multi-core processor), multipleprocessors 812, 814 with a single core, multiple processors 812, 814with multiple cores, or any combination thereof.

The memory/storage 806 may include memory, such as a main memory 816, orother memory storage, and a storage unit 818, both accessible to theprocessors 804 such as via the bus 810. The storage unit 818 and mainmemory 816 store the instructions 802 embodying any one or more of themethodologies or functions described herein. The instructions 802 mayalso reside, completely or partially, within the main memory 816, withinthe storage unit 818, within at least one of the processors 804 (e.g.,within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 800. Accordingly, themain memory 816, the storage unit 818, and the memory of processors 804are examples of machine-readable media. “Machine-readable media,” alsoreferred to herein as “computer-readable storage media”, in thiscontext, refers to a component, device, or other tangible media able tostore instructions 802 and data temporarily or permanently and mayinclude, but is not limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, optical media, magneticmedia, cache memory, other types of storage (e.g., erasable programmableread-only memory (EEPROM)) and/or any suitable combination thereof. Theterm “machine-readable medium” may be taken to include a single mediumor multiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store instructions 802. The term“machine-readable medium” shall also be taken to include any medium, orcombination of multiple media, that is capable of storing instructions802 (e.g., code) for execution by a machine 800, such that theinstructions 802, when executed by one or more processors 804 of themachine 800, cause the machine 800 to perform any one or more of themethodologies described herein. Accordingly, a “machine-readable medium”refers to a single storage apparatus or device, as well as “cloud-based”storage systems or storage networks that include multiple storageapparatus or devices. The term “machine-readable medium” excludessignals per se.

The I/O components 808 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 808 that are included in a particular machine 800 will dependon the type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components 808may include many other components that are not shown in FIG. 8. The I/Ocomponents 808 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example implementations, the I/O components 808 mayinclude user output components 820 and user input components 822. Theuser output components 820 may include visual components (e.g., adisplay such as a plasma display panel (PDP), a light emitting diode(LED) display, a liquid crystal display (LCD), a projector, or a cathoderay tube (CRT)), acoustic components (e.g., speakers), haptic components(e.g., a vibratory motor, resistance mechanisms), other signalgenerators, and so forth. The user input components 822 may includealphanumeric input components (e.g., a keyboard, a touch screenconfigured to receive alphanumeric input, a photo-optical keyboard, orother alphanumeric input components), point-based input components(e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, orother pointing instrument), tactile input components (e.g., a physicalbutton, a touch screen that provides location or force of touches ortouch gestures, or other tactile input components), audio inputcomponents (e.g., a microphone), and the like.

In further example implementations, the I/O components 808 may includebiometric components 824, motion components 826, environmentalcomponents 828, or position components 830 among a wide array of othercomponents. For example, the biometric components 824 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 826 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 828 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 830 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 808 may include communication components 832 operableto couple the machine 800 to a network 834 or devices 836. For example,the communication components 832 may include a network interfacecomponent or other suitable device to interface with the network 834. Infurther examples, communication components 832 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 836 may be another machine 800 or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 832 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 832 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components832, such as location via Internet Protocol (IP) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

“Component,” in this context, refers to a device, physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleimplementations, one or more computer systems (e.g., a standalonecomputer system, a client computer system, or a server computer system)or one or more hardware components of a computer system (e.g., aprocessor or a group of processors) may be configured by software (e.g.,an application or application portion) as a hardware component thatoperates to perform certain operations as described herein.

A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as afield-programmable gate array (FPGA) or an ASIC. A hardware componentmay also include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. For example, ahardware component may include software executed by a general-purposeprocessor 804 or other programmable processor. Once configured by suchsoftware, hardware components become specific machines (or specificcomponents of a machine 800) uniquely tailored to perform the configuredfunctions and are no longer general-purpose processors 804. It will beappreciated that the decision to implement a hardware componentmechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations. Accordingly, the phrase“hardware component” (or “hardware-implemented component”) should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. Consideringimplementations in which hardware components are temporarily configured(e.g., programmed), each of the hardware components need not beconfigured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processor 804processor 804 configured by software to become a special-purposeprocessor, the general-purpose processor 804 processor 804 may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor 812, 814 or processors804, for example, to constitute a particular hardware component at oneinstance of time and to constitute a different hardware component at adifferent instance of time.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inimplementations in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output.

Hardware components may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors 804 thatare temporarily configured (e.g., by software) or permanently configuredto perform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 804 may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors804. Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor 812, 814 orprocessors 804 being an example of hardware. For example, at least someof the operations of a method may be performed by one or more processors804 or processor-implemented components. Moreover, the one or moreprocessors 804 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 800 includingprocessors 804), with these operations being accessible via a network834 (e.g., the Internet) and via one or more appropriate interfaces(e.g., an API). The performance of certain of the operations may bedistributed among the processors, not only residing within a singlemachine 800, but deployed across a number of machines. In some exampleimplementations, the processors 804 or processor-implemented componentsmay be located in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleimplementations, the processors 804 or processor-implemented componentsmay be distributed across a number of geographic locations.

FIG. 9 is a block diagram illustrating system 900 that includes anexample software architecture 902, which may be used in conjunction withvarious hardware architectures herein described. FIG. 9 is anon-limiting example of a software architecture and it will beappreciated that many other architectures may be implemented tofacilitate the functionality described herein. The software architecture902 may execute on hardware such as machine 800 of FIG. 8 that includes,among other things, processors 804, memory/storage 806, and input/output(I/O) components 808. A representative hardware layer 904 hardware layer904 is illustrated and can represent, for example, the machine 800 ofFIG. 8. The representative hardware layer 904 hardware layer 904includes a processing unit 906 having associated executable instructions908. Executable instructions 908 represent the executable instructionsof the software architecture 902, including implementation of themethods, components, and so forth described herein. The hardware layer904 also includes at least one of memory or storage modulesmemory/storage 910, which also have executable instructions 908. Thehardware layer 904 may also comprise other hardware 912.

In the example architecture of FIG. 9, the software architecture 902 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 902 mayinclude layers such as an operating system 914, libraries 916,frameworks/middleware 918, applications 920, and a presentation layer922. Operationally, the applications 920 or other components within thelayers may invoke API calls 924 through the software stack and receivemessages 926 in response to the API calls 924. The layers illustratedare representative in nature and not all software architectures have alllayers. For example, some mobile or special purpose operating systemsmay not provide a frameworks/middleware 918, while others may providesuch a layer. Other software architectures may include additional ordifferent layers.

The operating system 914 may manage hardware resources and providecommon services. The operating system 914 may include, for example, akernel 928, services 930, and drivers 932. The kernel 928 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 928 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 930 may provideother common services for the other software layers. The drivers 932 areresponsible for controlling or interfacing with the underlying hardware.For instance, the drivers 932 include display drivers, camera drivers,Bluetooth® drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audiodrivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 916 provide a common infrastructure that is used by atleast one of the applications 920, other components, or layers. Thelibraries 916 provide functionality that allows other softwarecomponents to perform tasks in an easier fashion than to interfacedirectly with the underlying operating system 914 functionality (e.g.,kernel 928, services 930, drivers 932). The libraries 916 may includesystem libraries 934 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 916 may include API libraries 936 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia format such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to rendertwo-dimensional and three-dimensional in a graphic content on adisplay), database libraries (e.g., SQLite that may provide variousrelational database functions), web libraries (e.g., WebKit that mayprovide web browsing functionality), and the like. The libraries 916 mayalso include a wide variety of other libraries 938 to provide many otherAPIs to the applications 920 and other software components/modules.

The frameworks/middleware 918 (also sometimes referred to as middleware)provide a higher-level common infrastructure that may be used by theapplications 920 or other software components/modules. For example, theframeworks/middleware 918 may provide various graphical user interfacefunctions, high-level resource management, high-level location services,and so forth. The frameworks/middleware 918 may provide a broad spectrumof other APIs that may be utilized by the applications 920 or othersoftware components/modules, some of which may be specific to aparticular operating system 914 or platform.

The applications 920 include built-in applications 940 and third-partyapplications 942. Examples of representative built-in applications 940may include, but are not limited to, a contacts application, a browserapplication, a book reader application, a location application, a mediaapplication, a messaging application, or a game application. Third-partyapplications 942 may include an application developed using the ANDROID™or IOS™ software development kit (SDK) by an entity other than thevendor of the particular platform, and may be mobile software running ona mobile operating system such as IOS™, ANDROID™ WINDOWS® Phone, orother mobile operating systems. The third-party applications 942 mayinvoke the API calls 924 provided by the mobile operating system (suchas operating system 914) to facilitate functionality described herein.

The applications 920 may use built-in operating system functions (e.g.,kernel 928, services 930, drivers 932), libraries 916, andframeworks/middleware 918 to create UIs to interact with users of thesystem. Alternatively, or additionally, in some systems, interactionswith a user may occur through a presentation layer, such as presentationlayer 922. In these systems, the application/component “logic” can beseparated from the aspects of the application/component that interactwith a user.

Changes and modifications may be made to the disclosed implementationswithout departing from the scope of the present disclosure. These andother changes or modifications are intended to be included within thescope of the present disclosure, as expressed in the following claims.

What is claimed is:
 1. A method comprising: obtaining, by a computingsystem including one or more processing devices and one or more memorydevices, imaging data from one or more imaging data sources, the imagingdata corresponding to at least a portion of a face of an individual;generating, based on the imaging data, a plurality of images of the faceof the individual from a plurality of vantage points; determining that avantage point of an image included in the imaging data is different froma reference vantage point; modifying a first location of a first iris ofthe individual and a second location of a second iris of the individualto correct for a difference between the vantage point of the image andthe reference vantage point; determining, by the computing system andbased on the imaging data, a gaze deviation metric for the individualbased on a first image of the plurality of images corresponding to thevantage point and a second image of the plurality of imagescorresponding to the reference vantage point, the gaze deviation metricindicating an offset of a location of at least one iris of theindividual with respect to an expected iris location; analyzing, by thecomputing system and based on the imaging data, one or more targetregions of the face of the individual to determine a measure ofsimilarity between an appearance of the one or more target regions withrespect to an additional appearance of one or more additional targetregions located on an opposite side of the face of the individual;determining, by the computing system and based on the measure ofsimilarity, a facial paralysis metric of the individual, the facialparalysis metric indicating a measure of facial paralysis of theindividual; analyzing, by the computing system, at least one of the gazedeviation metric or the facial paralysis metric to determine anassessment metric indicating a probability of a presence of a strokewith respect to the individual; and generating, by the computing system,user interface data corresponding to one or more user interfaces thatinclude a user interface element indicating the probability of thepresence of the stroke with respect to the individual.
 2. The method ofclaim 1, comprising: obtaining, by the computing system, training datathat includes image training data, the image training data including: afirst set of images of first individuals that experienced a stroke thatresulted in an amount of gaze deviation; a second set of images ofsecond individuals that experienced a stroke that resulted in an amountof facial paralysis; and a third set of images of third individuals thatdid not experience a stroke, that did not have an amount of gazedeviation, and that did not have an amount of facial paralysis.
 3. Themethod of claim 2, comprising: analyzing, by the computing system, thesecond set of images and the third set of images to determine a numberof regions of faces of individuals that have different characteristicsbetween the second individuals that experienced a stroke and the firstindividuals that did not experience a stroke; and identifying, by thecomputing system, the one or more target regions from among the numberof regions.
 4. The method of claim 2, comprising: analyzing, by thecomputing system, the first set of images and the third set of images todetermine an expected gaze that corresponds to the third individualsthat did not experience a stroke; and analyzing, by the computingsystem, the second set of images and the third set of images todetermine an expected appearance of the one or more target regions withrespect to the third individuals that did not experience a stroke. 5.The method of claim 2, wherein the training data includes classificationdata that labels the first set of images as being obtained from thefirst individuals that experienced a stroke resulting in an amount ofgaze deviation, that labels the second set of images as being obtainedfrom the second individuals that experienced a stroke resulting in anamount of facial paralysis, and that labels the third set of images asbeing obtained from the third individuals that did not experience astroke.
 6. The method of claim 2, comprising: training, by the computingsystem and using the training data, one or more convolutional neuralnetworks to determine the facial paralysis metric and to determine thegaze deviation metric.
 7. The method of claim 6, wherein: input to theone or more convolutional neural networks during training includes thefirst set of images with classification data indicating that the firstindividuals experienced a stroke that resulted in an amount of gazedeviation and the third set of images with additional classificationdata indicating that the third individuals did not have an amount ofgaze deviation; and the one or more convolutional neural networks aretrained to provide output indicating a plurality of categories,individual categories of the plurality of categories corresponding todifferent amounts of gaze deviation.
 8. The method of claim 6, wherein:input to the one or more convolutional neural networks during trainingincludes the second set of images with classification data indicatingthat the second individuals experienced a stroke that resulted in anamount of facial paralysis and the third set of images with additionalclassification data indicating that the third individuals did not havean amount of facial paralysis; and the one or more convolutional neuralnetworks are trained to provide output indicating a plurality ofcategories, individual categories of the plurality of categoriescorresponding to different amounts of facial paralysis.
 9. The method ofclaim 1, comprising: determining, by the computing system, a location ofan occluded blood vessel of the individual based on at least one of thegaze deviation metric or the facial paralysis metric.
 10. The method ofclaim 1, wherein the one or more imaging sources includes acomputed-tomography based imaging apparatus or a magneticresonance-based imaging apparatus.
 11. The method of claim 1, whereinthe one or more imaging sources includes a mobile computing device andthe imaging data includes one or more images captured by the mobilecomputing device using a structured light pattern, using a time offlight camera, or using a LiDAR system.
 12. A system comprising: one ormore hardware processors; and one or more non-transitorycomputer-readable storage media including computer-readable instructionsthat, when executed by the one or more hardware processors, cause theone or more hardware processors to perform operations comprising:obtaining imaging data from one or more imaging sources, the imagingdata including at least a portion of a face of an individual;generating, based on the imaging data, a plurality of images of the faceof the individual from a plurality of vantage points; determining a gazedeviation metric for the individual based on a first image of theplurality of images corresponding to a first vantage point and a secondimage of the plurality of images corresponding to a second vantagepoint, the gaze deviation metric indicating an offset of a location ofat least one iris of the individual with respect to an expected irislocation; determining, based on the imaging data, a facial paralysismetric of the individual, the facial paralysis metric indicating ameasure of facial paralysis of the individual; analyzing at least one ofthe gaze deviation metric or the facial paralysis metric to determine anassessment metric indicating a probability of a presence of a strokewith respect to the individual; and generating user interface datacorresponding to one or more user interfaces that include a userinterface element indicating the probability of the presence of thestroke with respect to the individual.
 13. The system of claim 12,wherein: the imaging data is obtained from a magnetic resonance-basedimaging apparatus or a computed tomography-based imaging apparatus; theone or more non-transitory computer-readable storage media includeadditional computer-readable instructions that, when executed by the oneor more hardware processors, cause the one or more hardware processorsto perform additional operations comprising: generating a firstadditional image based on the imaging data that indicates blood vesselsof the brain of the individual; and generating a second additional imagebased on the imaging data that includes facial features of theindividual; and wherein a user interface of the one or more userinterfaces includes the first additional image and the second additionalimage.
 14. The system of claim 12, wherein: one or more target regionsinclude a first plurality of target regions located on a first side ofthe face of the individual and a second plurality of target regionslocated on a second side of the face of the individual, the first sideof the face being contralateral with respect to the second side of theface; and the one or more non-transitory computer-readable storage mediainclude additional computer-readable instructions that, when executed bythe one or more hardware processors, cause the one or more hardwareprocessors to perform additional operations comprising: determining theexpected appearance of the one or more target regions based on firstcharacteristics of the first plurality of target regions; and the facialparalysis metric is determined based on differences between the firstcharacteristics of the first plurality of target regions and secondcharacteristics of the second plurality of target regions.
 15. Thesystem of claim 12, wherein the one or more non-transitorycomputer-readable storage media include additional computer-readableinstructions that, when executed by the one or more hardware processors,cause the one or more hardware processors to perform additionaloperations comprising: obtaining a reference image of the individualthat is captured prior to a suspected incidence of a stroke occurringwith respect to the individual; determining the expected iris locationbased on a first location of a first iris of the individual in thereference image and a second location of a second iris of the individualin the reference image; and determining the expected appearance of theone or more target regions based on an appearance of the one or moretarget regions in the reference image.
 16. The system of claim 12,wherein the one or more non-transitory computer-readable storage mediainclude additional computer-readable instructions that, when executed bythe one or more hardware processors, cause the one or more hardwareprocessors to perform additional operations comprising: determining anangular offset between a location of an iris of the individualdetermined based on the imaging data and the expected iris location;wherein the gaze deviation metric is based on the angular offset. 17.The system of claim 12, wherein the one or more non-transitorycomputer-readable storage media include additional computer-readableinstructions that, when executed by the one or more hardware processors,cause the one or more hardware processors to perform additionaloperations comprising: determining the facial paralysis metric using thefirst image of the plurality of images corresponding to the firstvantage point and the second image of the plurality of imagescorresponding to the second vantage point.
 18. One or morenon-transitory computer-readable media storing computer readableinstructions that, when executed by one or more processing devices,cause the one or more processing devices to perform operationscomprising: obtaining imaging data from one or more imaging sources, theimaging data including at least a portion of a face of an individual;determining that a vantage point of an image included in the imagingdata is different from a reference vantage point; modifying a firstlocation of a first iris of the individual and a second location of asecond iris of the individual to correct for a difference between thevantage point of the image and the reference vantage point; determining,based on the imaging data, a gaze deviation metric for the individualaccording to a modified first location of the first iris and a modifiedlocation of the second iris, the gaze deviation metric indicating anoffset of a location of at least one iris of the individual with respectto an expected iris location; analyzing, based on the imaging data, oneor more target regions of the face of the individual to determine ameasure of similarity between an appearance of the one or more targetregions with respect to at least one of an additional appearance of theone or more target regions included in a reference image or anadditional appearance of one or more additional target regions locatedon an opposite side of the face of the individual; determining, based onthe measure of similarity, a facial paralysis metric of the individual,the facial paralysis metric indicating a measure of facial paralysis ofthe individual; analyzing at least one of the gaze deviation metric orthe facial paralysis metric to determine an assessment metric indicatinga probability of a presence of a stroke with respect to the individual;and generating user interface data corresponding to one or more userinterfaces that include a user interface element indicating theprobability of the presence of the stroke with respect to theindividual.
 19. The one or more non-transitory computer-readable mediaof claim 18, storing additional computer-readable instructions that,when executed by the one or more processing devices, cause the one ormore processing devices to perform additional operations comprising:determining a binary mask that indicates first portions of the imagingdata that correspond to a foreground and second portions of the imagingdata that correspond a background; generating a mesh of polygons thatcorrespond to a surface of the face of the individual; and rendering themesh of polygons to generate an image of the face of the individual. 20.The one or more non-transitory computer-readable media of claim 18,storing additional computer-readable instructions that, when executed bythe one or more processing devices, cause the one or more processingdevices to perform additional operations comprising: performing one ormore segmentation processes with respect to the imaging data to identifyeyes of the individual.